Several recent works have proposed extending diffusion models towards generating protein structures. These predominantly perform diffusion on the 3D Cartesian coordinates of the residues themselves. For example, [1]} use an E(3)-equivariant graph neural network to model the coordinates of protein residues. [2]} adopt a hybrid approach where they train an equivariant transformer with invariant point attention [3]}; this model generates the 3D coordinates of \(C_\alpha \)  atoms, the amino acid sequence, and the angles defining the orientation of side chains. Another recent work by [4]} performs diffusion for generating antibody fragments' structure and sequence by modeling 3D coordinates using an equivariant neural network. Note that these prior works all use some form of equivariance to translation, rotation, and/or reflection due to their formulation of diffusion on Cartesian coordinates. Another method, ProteinSGM, [5]} implements a score-based diffusion model that generates image-like square matrices describing pairwise angles and distances between all residues in an amino acid chain. However, this set of values is highly over-constrained, and must be used as a set of input constraints for Rosetta's folding algorithm [6]}, which in turn produces the final folded output. This is a similar approach to [7]}, and is likewise subject to the aforementioned concerns regarding complexity, satisfiability, and cleanliness of predicted constraints. Our work instead uses a minimal set of angles required to specify a protein backbone, and thus directly generates structures without relying on additional methods for refinement. Unfortunately, none of these prior works have publicly-available code, model weights, or generated examples at the time of this writing. Thus, our ability to perform direct qualitative and quantitative comparisons is limited.
